7 Refreshing Materialized Views

This chapter discusses how to refresh materialized views, which is a key element in maintaining good performance and consistent data when working with materialized views in a data warehousing environment.

About Refreshing Materialized Views

The database maintains data in materialized views by refreshing them after changes to the base tables.

Performing a refresh operation requires temporary space to rebuild the indexes and can require additional space for performing the refresh operation itself. Some sites might prefer not to refresh all of their materialized views at the same time: as soon as some underlying detail data has been updated, all materialized views using this data become stale. Therefore, if you defer refreshing your materialized views, you can either rely on your chosen rewrite integrity level to determine whether or not a stale materialized view can be used for query rewrite, or you can temporarily disable query rewrite with an ALTERSYSTEMSETQUERY_REWRITE_ENABLED = FALSE statement. After refreshing the materialized views, you can re-enable query rewrite as the default for all sessions in the current database instance by specifying ALTERSYSTEMSETQUERY_REWRITE_ENABLED as TRUE. Refreshing a materialized view automatically updates all of its indexes. In the case of full refresh, this requires temporary sort space to rebuild all indexes during refresh. This is because the full refresh truncates or deletes the table before inserting the new full data volume. If insufficient temporary space is available to rebuild the indexes, then you must explicitly drop each index or mark it UNUSABLE prior to performing the refresh operation.

About Types of Refresh for Materialized Views

The refresh method can be incremental or a complete refresh. There are two incremental refresh methods, known as log-based refresh and partition change tracking (PCT) refresh. The incremental refresh is commonly called FAST refresh as it usually performs faster than the complete refresh.

A complete refresh occurs when the materialized view is initially created when it is defined as BUILDIMMEDIATE, unless the materialized view references a prebuilt table or is defined as BUILDDEFERRED. Users can perform a complete refresh at any time after the materialized view is created. The complete refresh involves executing the query that defines the materialized view. This process can be slow, especially if the database must read and process huge amounts of data.

An incremental refresh eliminates the need to rebuild materialized views from scratch. Thus, processing only the changes can result in a very fast refresh time. Materialized views can be refreshed either on demand or at regular time intervals. Alternatively, materialized views in the same database as their base tables can be refreshed whenever a transaction commits its changes to the base tables.

For materialized views that use the log-based fast refresh method, a materialized view log and/or a direct loader log keep a record of changes to the base tables. A materialized view log is a schema object that records changes to a base table so that a materialized view defined on the base table can be refreshed incrementally. Each materialized view log is associated with a single base table. The materialized view log resides in the same database and schema as its base table.

The PCT refresh method can be used if the modified base tables are partitioned and the modified base table partitions can be used to identify the affected partitions or portions of data in the materialized view. When there have been some partition maintenance operations on the base tables, this is the only incremental refresh method that can be used. The PCT refresh removes all data in the affected materialized view partitions or affected portions of data and recomputes them from scratch.

When creating a materialized view, you have the option of specifying whether the refresh occurs ONDEMAND or ONCOMMIT.

If you anticipate performing insert, update or delete operations on tables referenced by a materialized view concurrently with the refresh of that materialized view, and that materialized view includes joins and aggregation, Oracle recommends you use ONCOMMIT fast refresh rather than ONDEMAND fast refresh.

In the case of ONCOMMIT, the materialized view is changed every time a transaction commits, thus ensuring that the materialized view always contains the latest data. Alternatively, you can control the time when refresh of the materialized views occurs by specifying ONDEMAND. In the case of ONDEMAND materialized views, the refresh can be performed with refresh methods provided in either the DBMS_SYNC_REFRESH or the DBMS_MVIEW packages:

The DBMS_SYNC_REFRESH package contains the APIs for synchronous refresh, a new refresh method introduced in Oracle Database 12c, Release 1. For details, see Synchronous Refresh.

The DBMS_MVIEW package contains the APIs whose usage is described in this chapter. There are three basic types of refresh operations: complete refresh, fast refresh, and partition change tracking (PCT) refresh. These basic types have been enhanced in Oracle Database 12c, Release 1 with a new refresh option called out-of-place refresh.

The DBMS_MVIEW package contains three APIs for performing refresh operations:

DBMS_MVIEW.REFRESH

Refresh one or more materialized views.

DBMS_MVIEW.REFRESH_ALL_MVIEWS

Refresh all materialized views.

DBMS_MVIEW.REFRESH_DEPENDENT

Refresh all materialized views that depend on a specified master table or materialized view or list of master tables or materialized views.

For each of these refresh options, you have two techniques for how the refresh is performed, namely in-place refresh and out-of-place refresh. The in-place refresh executes the refresh statements directly on the materialized view. The out-of-place refresh creates one or more outside tables and executes the refresh statements on the outside tables and then switches the materialized view or affected materialized view partitions with the outside tables. Both in-place refresh and out-of-place refresh achieve good performance in certain refresh scenarios. However, the out-of-place refresh enables high materialized view availability during refresh, especially when refresh statements take a long time to finish.

Also adopting the out-of-place mechanism, a new refresh method called synchronous refresh is introduced in Oracle Database 12c, Release 1. It targets the common usage scenario in the data warehouse where both fact tables and their materialized views are partitioned in the same way or their partitions are related by a functional dependency.

The refresh approach enables you to keep a set of tables and the materialized views defined on them to be always in sync. In this refresh method, the user does not directly modify the contents of the base tables but must use the APIs provided by the synchronous refresh package that will apply these changes to the base tables and materialized views at the same time to ensure their consistency. The synchronous refresh method is well-suited for data warehouses, where the loading of incremental data is tightly controlled and occurs at periodic intervals.

About Complete Refresh for Materialized Views

A complete refresh occurs when the materialized view is initially defined as BUILDIMMEDIATE, unless the materialized view references a prebuilt table. For materialized views using BUILDDEFERRED, a complete refresh must be requested before it can be used for the first time. A complete refresh may be requested at any time during the life of any materialized view. The refresh involves reading the detail tables to compute the results for the materialized view. This can be a very time-consuming process, especially if there are huge amounts of data to be read and processed. Therefore, you should always consider the time required to process a complete refresh before requesting it.

There are, however, cases when the only refresh method available for an already built materialized view is complete refresh because the materialized view does not satisfy the conditions specified in the following section for a fast refresh.

About Fast Refresh for Materialized Views

Most data warehouses have periodic incremental updates to their detail data. As described in "About Materialized View Schema Design", you can use the SQL*Loader or any bulk load utility to perform incremental loads of detail data. Fast refresh of your materialized views is usually efficient, because instead of having to recompute the entire materialized view, the changes are applied to the existing data. Thus, processing only the changes can result in a very fast refresh time.

About Partition Change Tracking (PCT) Refresh for Materialized Views

When there have been some partition maintenance operations on the detail tables, this is the only method of fast refresh that can be used. PCT-based refresh on a materialized view is enabled only if all the conditions described in "About Partition Change Tracking" are satisfied.

In the absence of partition maintenance operations on detail tables, when you request a FAST method (method => 'F') of refresh through procedures in DBMS_MVIEW package, Oracle uses a heuristic rule to try log-based rule fast refresh before choosing PCT refresh. Similarly, when you request a FORCE method (method => '?'), Oracle chooses the refresh method based on the following attempt order: log-based fast refresh, PCT refresh, and complete refresh. Alternatively, you can request the PCT method (method => 'P'), and Oracle uses the PCT method provided all PCT requirements are satisfied.

About the Out-of-Place Refresh Option

Beginning with Oracle Database 12c Release 1, a new refresh option is available to improve materialized view refresh performance and availability. This refresh option is called out-of-place refresh because it uses outside tables during refresh as opposed to the existing "in-place" refresh that directly applies changes to the materialized view container table. The out-of-place refresh option works with all existing refresh methods, such as FAST ('F'), COMPLETE ('C'), PCT ('P'), and FORCE ('?'). Out-of-place refresh is particularly effective when handling situations with large amounts of data changes, where conventional DML statements do not scale well. It also enables you to achieve a very high degree of availability because the materialized views that are being refreshed can be used for direct access and query rewrite during the execution of refresh statements. In addition, it helps to avoid potential problems such as materialized view container tables becoming fragmented over time or intermediate refresh results being seen.

In out-of-place refresh, the entire or affected portions of a materialized view are computed into one or more outside tables. For partitioned materialized views, if partition level change tracking is possible, and there are local indexes defined on the materialized view, the out-of-place method also builds the same local indexes on the outside tables. This refresh process is completed by either switching between the materialized view and the outside table or partition exchange between the affected partitions and the outside tables. Note that query rewrite is not supported during the switching or partition exchange operation. During refresh, the outside table is populated by direct load, which is efficient.

Types of Out-of-Place Refresh

This offers better availability than in-place fast refresh. It also offers better performance when changes affect a large part of the materialized view.

out-of-place PCT refresh

This offers better availability than in-place PCT refresh. There are two different approaches for partitioned and non-partitioned materialized views. If truncation and direct load are not feasible, you should use out-of-place refresh when the changes are relatively large. If truncation and direct load are feasible, in-place refresh is preferable in terms of performance. In terms of availability, out-of-place refresh is always preferable.

out-of-place complete refresh

This offers better availability than in-place complete refresh.

Using the refresh interface in the DBMS_MVIEW package, with method=? and out_of_place=true, out-of-place fast refresh are attempted first, then out-of-place PCT refresh, and finally out-of-place complete refresh. An example is the following:

Not permitted if materialized view logs, triggers, or constraints (except NOTNULL) are defined on the materialized view

Not permitted if the materialized view contains the CLUSTERING clause

Not applied to complete refresh within a CREATE or ALTERMATERIALIZEDVIEW session or an ALTERTABLE session

Atomic mode is not permitted. If you specify atomic_refresh as TRUE and out_of_place as TRUE, an error is displayed

For out-of-place PCT refresh, there is the following restriction:

No UNIONALL or grouping sets are permitted

For out-of-place fast refresh, there are the following restrictions:

No UNIONALL, grouping sets or outer joins are permitted

Not allowed for materialized join views when more than one base table is modified with mixed DML statements

Out-of-place refresh requires additional storage for the outside table and the indexes for the duration of the refresh. Thus, you must have enough available tablespace or auto extend turned on.

The partition exchange in out-of-place PCT refresh impacts the global index on the materialized view. Therefore, if there are global indexes defined on the materialized view container table, Oracle disables the global indexes before doing the partition exchange and rebuild the global indexes after the partition exchange. This rebuilding is additional overhead.

About ON COMMIT Refresh for Materialized Views

A materialized view can be refreshed automatically using the ONCOMMIT method. Therefore, whenever a transaction commits which has updated the tables on which a materialized view is defined, those changes are automatically reflected in the materialized view. The advantage of using this approach is you never have to remember to refresh the materialized view. The only disadvantage is the time required to complete the commit will be slightly longer because of the extra processing involved. However, in a data warehouse, this should not be an issue because there is unlikely to be concurrent processes trying to update the same table.

About Manual Refresh Using the DBMS_MVIEW Package

When a materialized view is refreshed ONDEMAND, one of four refresh methods can be specified as shown in the following table. You can define a default option during the creation of the materialized view. Table 7-1 details the refresh options.

Table 7-1 ON DEMAND Refresh Methods

Refresh Method

Parameter

Description

COMPLETE

C

Refreshes by recalculating the defining query of the materialized view.

FAST

F

Refreshes by incrementally applying changes to the materialized view.

For local materialized views, it chooses the refresh method which is estimated by optimizer to be most efficient. The refresh methods considered are log-based FAST and FAST_PCT.

FAST_PCT

P

Refreshes by recomputing the rows in the materialized view affected by changed partitions in the detail tables.

FORCE

?

Attempts a fast refresh. If that is not possible, it does a complete refresh.

For local materialized views, it chooses the refresh method which is estimated by optimizer to be most efficient. The refresh methods considered are log based FAST, FAST_PCT, and COMPLETE.

Three refresh procedures are available in the DBMS_MVIEW package for performing ONDEMAND refresh. Each has its own unique set of parameters.

Refreshing Specific Materialized Views with REFRESH

Use the DBMS_MVIEW.REFRESH procedure to refresh one or more materialized views. Some parameters are used only for replication, so they are not mentioned here. The required parameters to use this procedure are:

The comma-delimited list of materialized views to refresh

The refresh method: F-Fast, P-Fast_PCT, ?-Force, C-Complete

The rollback segment to use

Refresh after errors (TRUE or FALSE)

A Boolean parameter. If set to TRUE, the number_of_failures output parameter is set to the number of refreshes that failed, and a generic error message indicates that failures occurred. The alert log for the instance gives details of refresh errors. If set to FALSE, the default, then refresh stops after it encounters the first error, and any remaining materialized views in the list are not refreshed.

The following four parameters are used by the replication process. For warehouse refresh, set them to FALSE, 0,0,0.

Atomic refresh (TRUE or FALSE)

If set to TRUE, then all refreshes are done in one transaction. If set to FALSE, then each of the materialized views is refreshed non-atomically in separate transactions. If set to FALSE, Oracle can optimize refresh by using parallel DML and truncate DDL on a materialized views. When a materialized view is refreshed in atomic mode, it is eligible for query rewrite if the rewrite integrity mode is set to stale_tolerated. Atomic refresh cannot be guaranteed when refresh is performed on nested views.

Whether to use out-of-place refresh

This parameter works with all existing refresh methods (F, P, C, ?). So, for example, if you specify F and out_of_place=true, then an out-of-place fast refresh is attempted. Similarly, if you specify P and out_of_place=true, then out-of-place PCT refresh is attempted.

For example, to perform a fast refresh on the materialized view cal_month_sales_mv, the DBMS_MVIEW package would be called as follows:

Multiple materialized views can be refreshed at the same time, and they do not all have to use the same refresh method. To give them different refresh methods, specify multiple method codes in the same order as the list of materialized views (without commas). For example, the following specifies that cal_month_sales_mv be completely refreshed and fweek_pscat_sales_mv receive a fast refresh:

If the refresh method is not specified, the default refresh method as specified in the materialized view definition is used.

Refreshing All Materialized Views with REFRESH_ALL_MVIEWS

An alternative to specifying the materialized views to refresh is to use the procedure DBMS_MVIEW.REFRESH_ALL_MVIEWS. This procedure refreshes all materialized views. If any of the materialized views fails to refresh, then the number of failures is reported.

The parameters for this procedure are:

The number of failures (this is an OUT variable)

The refresh method: F-Fast, P-Fast_PCT, ?-Force, C-Complete

Refresh after errors (TRUE or FALSE)

A Boolean parameter. If set to TRUE, the number_of_failures output parameter is set to the number of refreshes that failed, and a generic error message indicates that failures occurred. The alert log for the instance gives details of refresh errors. If set to FALSE, the default, then refresh stops after it encounters the first error, and any remaining materialized views in the list is not refreshed.

Atomic refresh (TRUE or FALSE)

If set to TRUE, then all refreshes are done in one transaction. If set to FALSE, then each of the materialized views is refreshed non-atomically in separate transactions. If set to FALSE, Oracle can optimize refresh by using parallel DML and truncate DDL on a materialized views. When a materialized view is refreshed in atomic mode, it is eligible for query rewrite if the rewrite integrity mode is set to stale_tolerated. Atomic refresh cannot be guaranteed when refresh is performed on nested views.

Whether to use out-of-place refresh

This parameter works with all existing refresh method (F, P, C, ?). So, for example, if you specify F and out_of_place=true, then an out-of-place fast refresh is attempted. Similarly, if you specify P and out_of_place=true, then out-of-place PCT refresh is attempted.

An example of refreshing all materialized views is the following:

DBMS_MVIEW.REFRESH_ALL_MVIEWS(failures,'C','', TRUE, FALSE, FALSE);

Refreshing Dependent Materialized Views with REFRESH_DEPENDENT

The third procedure, DBMS_MVIEW.REFRESH_DEPENDENT, refreshes only those materialized views that depend on a specific table or list of tables. For example, suppose the changes have been received for the orders table but not for customer payments. The refresh dependent procedure can be called to refresh only those materialized views that reference the orders table.

The parameters for this procedure are:

The number of failures (this is an OUT variable)

The dependent table

The refresh method: F-Fast, P-Fast_PCT, ?-Force, C-Complete

The rollback segment to use

Refresh after errors (TRUE or FALSE)

A Boolean parameter. If set to TRUE, the number_of_failures output parameter is set to the number of refreshes that failed, and a generic error message indicates that failures occurred. The alert log for the instance gives details of refresh errors. If set to FALSE, the default, then refresh stops after it encounters the first error, and any remaining materialized views in the list are not refreshed.

Atomic refresh (TRUE or FALSE)

If set to TRUE, then all refreshes are done in one transaction. If set to FALSE, then each of the materialized views is refreshed non-atomically in separate transactions. If set to FALSE, Oracle can optimize refresh by using parallel DML and truncate DDL on a materialized views. When a materialized view is refreshed in atomic mode, it is eligible for query rewrite if the rewrite integrity mode is set to stale_tolerated. Atomic refresh cannot be guaranteed when refresh is performed on nested views.

Whether it is nested or not

If set to TRUE, refresh all the dependent materialized views of the specified set of tables based on a dependency order to ensure the materialized views are truly fresh with respect to the underlying base tables.

Whether to use out-of-place refresh

This parameter works with all existing refresh methods (F, P, C, ?). So, for example, if you specify F and out_of_place=true, then an out-of-place fast refresh is attempted. Similarly, if you specify P and out_of_place=true, then out-of-place PCT refresh is attempted.

To perform a full refresh on all materialized views that reference the customers table, specify:

About Using Job Queues for Refresh

Job queues can be used to refresh multiple materialized views in parallel. If queues are not available, fast refresh sequentially refreshes each view in the foreground process. To make queues available, you must set the JOB_QUEUE_PROCESSES parameter. This parameter defines the number of background job queue processes and determines how many materialized views can be refreshed concurrently. Oracle tries to balance the number of concurrent refreshes with the degree of parallelism of each refresh. The order in which the materialized views are refreshed is determined by dependencies imposed by nested materialized views and potential for efficient refresh by using query rewrite against other materialized views (See "Scheduling Refresh of Materialized Views" for details). This parameter is only effective when atomic_refresh is set to FALSE.

If the process that is executing DBMS_MVIEW.REFRESH is interrupted or the instance is shut down, any refresh jobs that were executing in job queue processes are requeued and continue running. To remove these jobs, use the DBMS_JOB.REMOVE procedure.

When Fast Refresh is Possible

Not all materialized views may be fast refreshable. Therefore, use the package DBMS_MVIEW.EXPLAIN_MVIEW to determine what refresh methods are available for a materialized view.

If you are not sure how to make a materialized view fast refreshable, you can use the DBMS_ADVISOR.TUNE_MVIEW procedure, which provides a script containing the statements required to create a fast refreshable materialized view.

Recommended Initialization Parameters for Parallelism

The following initialization parameters need to be set properly for parallelism to be effective:

PARALLEL_MAX_SERVERS should be set high enough to take care of parallelism. You must consider the number of slaves needed for the refresh statement. For example, with a degree of parallelism of eight, you need 16 slave processes.

PGA_AGGREGATE_TARGET should be set for the instance to manage the memory usage for sorts and joins automatically. If the memory parameters are set manually, SORT_AREA_SIZE should be less than HASH_AREA_SIZE.

To display partition information for the detail table a materialized view is based on.

*_USER_MVIEW_DETAIL_PARTITION

To determine which partitions are fresh.

*_USER_MVIEW_DETAIL_SUBPARTITION

To determine which subpartitions are fresh.

The use of these views is illustrated in the following examples. Figure 7-1 illustrates a range-list partitioned table and a materialized view based on it. The partitions are P1, P2, P3, and P4, while the subpartitions are SP1, SP2, and SP3.

Scheduling Refresh of Materialized Views

Very often you have multiple materialized views in the database. Some of these can be computed by rewriting against others. This is very common in data warehousing environment where you may have nested materialized views or materialized views at different levels of some hierarchy.

In such cases, you should create the materialized views as BUILD DEFERRED, and then issue one of the refresh procedures in DBMS_MVIEW package to refresh all the materialized views. Oracle Database computes the dependencies and refreshes the materialized views in the right order. Consider the example of a complete hierarchical cube described in "Examples of Hierarchical Cube Materialized Views". Suppose all the materialized views have been created as BUILD DEFERRED. Creating the materialized views as BUILD DEFERRED only creates the metadata for all the materialized views. And, then, you can just call one of the refresh procedures in DBMS_MVIEW package to refresh all the materialized views in the right order:

The procedure refreshes the materialized views in the order of their dependencies (first sales_hierarchical_mon_cube_mv, followed by sales_hierarchical_qtr_cube_mv, then, sales_hierarchical_yr_cube_mv and finally, sales_hierarchical_all_cube_mv). Each of these materialized views gets rewritten against the one prior to it in the list).

The same kind of rewrite can also be used while doing PCT refresh. PCT refresh recomputes rows in a materialized view corresponding to changed rows in the detail tables. And, if there are other fresh materialized views available at the time of refresh, it can go directly against them as opposed to going against the detail tables.

Hence, it is always beneficial to pass a list of materialized views to any of the refresh procedures in DBMS_MVIEW package (irrespective of the method specified) and let the procedure figure out the order of doing refresh on materialized views.

Tips for Refreshing Materialized Views

This section contains the following topics with tips on refreshing materialized views:

Tips for Refreshing Materialized Views with Aggregates

Following are some guidelines for using the refresh mechanism for materialized views with aggregates.

For fast refresh, create materialized view logs on all detail tables involved in a materialized view with the ROWID, SEQUENCE and INCLUDINGNEWVALUES clauses.

Include all columns from the table likely to be used in materialized views in the materialized view logs.

Fast refresh may be possible even if the SEQUENCE option is omitted from the materialized view log. If it can be determined that only inserts or deletes will occur on all the detail tables, then the materialized view log does not require the SEQUENCE clause. However, if updates to multiple tables are likely or required or if the specific update scenarios are unknown, make sure the SEQUENCE clause is included.

Use Oracle's bulk loader utility or direct-path INSERT (INSERT with the APPEND hint for loads). Starting in Oracle Database 12c, the database automatically gathers table statistics as part of a bulk-load operation (CTAS and IAS) similar to how statistics are gathered when an index is created. By gathering statistics during the data load, you avoid additional scan operations and provide the necessary statistics as soon as the data becomes available to the users. Note that, in the case of an IAS statement, statistics are only gathered if the table the data is being inserted into is empty.

This is a lot more efficient than conventional insert. During loading, disable all constraints and re-enable when finished loading. Note that materialized view logs are required regardless of whether you use direct load or conventional DML.

Try to optimize the sequence of conventional mixed DML operations, direct-path INSERT and the fast refresh of materialized views. You can use fast refresh with a mixture of conventional DML and direct loads. Fast refresh can perform significant optimizations if it finds that only direct loads have occurred, as illustrated in the following:

You can use fast refresh with conventional mixed DML (INSERT, UPDATE, and DELETE) to the detail tables. However, fast refresh is able to perform significant optimizations in its processing if it detects that only inserts or deletes have been done to the tables, such as:

DML INSERT or DELETE to the detail table

Refresh materialized views

DML update to the detail table

Refresh materialized view

Even more optimal is the separation of INSERT and DELETE.

If possible, refresh should be performed after each type of data change (as shown earlier) rather than issuing only one refresh at the end. If that is not possible, restrict the conventional DML to the table to inserts only, to get much better refresh performance. Avoid mixing deletes and direct loads.

Furthermore, for refresh ONCOMMIT, Oracle keeps track of the type of DML done in the committed transaction. Therefore, do not perform direct-path INSERT and DML to other tables in the same transaction, as Oracle may not be able to optimize the refresh phase.

For ONCOMMIT materialized views, where refreshes automatically occur at the end of each transaction, it may not be possible to isolate the DML statements, in which case keeping the transactions short will help. However, if you plan to make numerous modifications to the detail table, it may be better to perform them in one transaction, so that refresh of the materialized view is performed just once at commit time rather than after each update.

Oracle recommends partitioning the tables because it enables you to use:

Parallel DML

For large loads or refresh, enabling parallel DML helps shorten the length of time for the operation.

Partitioning the materialized view also helps refresh performance as refresh can update the materialized view using parallel DML. For example, assume that the detail tables and materialized view are partitioned and have a parallel clause. The following sequence would enable Oracle to parallelize the refresh of the materialized view.

Bulk load into the detail table.

Enable parallel DML with an ALTERSESSIONENABLEPARALLELDML statement.

Refresh the materialized view.

For refresh using DBMS_MVIEW.REFRESH, set the parameter atomic_refresh to FALSE.

For COMPLETE refresh, this causes a TRUNCATE to delete existing rows in the materialized view, which is faster than a delete.

For PCT refresh, if the materialized view is partitioned appropriately, this uses TRUNCATEPARTITION to delete rows in the affected partitions of the materialized view, which is faster than a delete.

For FAST or FORCE refresh, if COMPLETE or PCT refresh is chosen, this is able to use the TRUNCATE optimizations described earlier.

When using DBMS_MVIEW.REFRESH with JOB_QUEUES, remember to set atomic to FALSE. Otherwise, JOB_QUEUES is not used. Set the number of job queue processes greater than the number of processors.

If job queues are enabled and there are many materialized views to refresh, it is faster to refresh all of them in a single command than to call them individually.

Use REFRESHFORCE to ensure refreshing a materialized view so that it can definitely be used for query rewrite. The best refresh method is chosen. If a fast refresh cannot be done, a complete refresh is performed.

Refresh all the materialized views in a single procedure call. This gives Oracle an opportunity to schedule refresh of all the materialized views in the right order taking into account dependencies imposed by nested materialized views and potential for efficient refresh by using query rewrite against other materialized views.

Tips for Refreshing Materialized Views Without Aggregates

If a materialized view contains joins but no aggregates, then having an index on each of the join column rowids in the detail table enhances refresh performance greatly, because this type of materialized view tends to be much larger than materialized views containing aggregates. For example, consider the following materialized view:

Indexes should be created on columns sales_rid, times_rid and cust_rid. Partitioning is highly recommended, as is enabling parallel DML in the session before invoking refresh, because it greatly enhances refresh performance.

This type of materialized view can also be fast refreshed if DML is performed on the detail table. It is recommended that the same procedure be applied to this type of materialized view as for a single table aggregate. That is, perform one type of change (direct-path INSERT or DML) and then refresh the materialized view. This is because Oracle Database can perform significant optimizations if it detects that only one type of change has been done.

Also, Oracle recommends that the refresh be invoked after each table is loaded, rather than load all the tables and then perform the refresh.

For refresh ONCOMMIT, Oracle keeps track of the type of DML done in the committed transaction. Oracle therefore recommends that you do not perform direct-path and conventional DML to other tables in the same transaction because Oracle may not be able to optimize the refresh phase. For example, the following is not recommended:

Direct load new data into the fact table

DML into the store table

Commit

Also, try not to mix different types of conventional DML statements if possible. This would again prevent using various optimizations during fast refresh. For example, try to avoid the following:

Insert into the fact table

Delete from the fact table

Commit

If many updates are needed, try to group them all into one transaction because refresh is performed just once at commit time, rather than after each update.

In a data warehousing environment, assuming that the materialized view has a parallel clause, the following sequence of steps is recommended:

Bulk load into the fact table

Enable parallel DML

An ALTERSESSIONENABLEPARALLELDML statement

Refresh the materialized view

Tips for Refreshing Nested Materialized Views

All underlying objects are treated as ordinary tables when refreshing materialized views. If the ONCOMMIT refresh option is specified, then all the materialized views are refreshed in the appropriate order at commit time. In other words, Oracle builds a partially ordered set of materialized views and refreshes them such that, after the successful completion of the refresh, all the materialized views are fresh. The status of the materialized views can be checked by querying the appropriate USER_, DBA_, or ALL_MVIEWS view.

If any of the materialized views are defined as ONDEMAND refresh (irrespective of whether the refresh method is FAST, FORCE, or COMPLETE), you must refresh them in the correct order (taking into account the dependencies between the materialized views) because the nested materialized view are refreshed with respect to the current contents of the other materialized views (whether fresh or not). This can be achieved by invoking the refresh procedure against the materialized view at the top of the nested hierarchy and specifying the nested parameter as TRUE.

If a refresh fails during commit time, the list of materialized views that has not been refreshed is written to the alert log, and you must manually refresh them along with all their dependent materialized views.

Use the same DBMS_MVIEW procedures on nested materialized views that you use on regular materialized views.

These procedures have the following behavior when used with nested materialized views:

If REFRESH is applied to a materialized view my_mv that is built on other materialized views, then my_mv is refreshed with respect to the current contents of the other materialized views (that is, the other materialized views are not made fresh first) unless you specify nested => TRUE.

If REFRESH_DEPENDENT is applied to materialized view my_mv, then only materialized views that directly depend on my_mv are refreshed (that is, a materialized view that depends on a materialized view that depends on my_mv will not be refreshed) unless you specify nested => TRUE.

If REFRESH_ALL_MVIEWS is used, the order in which the materialized views are refreshed is guaranteed to respect the dependencies between nested materialized views.

GET_MV_DEPENDENCIES provides a list of the immediate (or direct) materialized view dependencies for an object.

Tips for Fast Refresh with UNION ALL

You can use fast refresh for materialized views that use the UNIONALL operator by providing a maintenance column in the definition of the materialized view. For example, a materialized view with a UNIONALL operator can be made fast refreshable as follows:

The form of a maintenance marker column, column MARKER in the example, must be numeric_or_string_literalAScolumn_alias, where each UNIONALL member has a distinct value for numeric_or_string_literal.

Tips for Fast Refresh with Commit SCN-Based Materialized View Logs

You can often improve fast refresh performance by ensuring that your materialized view logs on the base table contain a WITHCOMMITSCN clause, often significantly. By optimizing materialized view log processing WITHCOMMITSCN, the fast refresh process can save time. The following example illustrates how to use this clause:

Note that only new materialized view logs can take advantage of COMMITSCN. Existing materialized view logs cannot be altered to add COMMITSCN unless they are dropped and recreated.

When a materialized view is created on both base tables with timestamp-based materialized view logs and base tables with commit SCN-based materialized view logs, an error (ORA-32414) is raised stating that materialized view logs are not compatible with each other for fast refresh.

Tips After Refreshing Materialized Views

After you have performed a load or incremental load and rebuilt the detail table indexes, you must re-enable integrity constraints (if any) and refresh the materialized views and materialized view indexes that are derived from that detail data. In a data warehouse environment, referential integrity constraints are normally enabled with the NOVALIDATE or RELY options. An important decision to make before performing a refresh operation is whether the refresh needs to be recoverable. Because materialized view data is redundant and can always be reconstructed from the detail tables, it might be preferable to disable logging on the materialized view. To disable logging and run incremental refresh non-recoverably, use the ALTERMATERIALIZEDVIEW ... NOLOGGING statement prior to refreshing.

If the materialized view is being refreshed using the ONCOMMIT method, then, following refresh operations, consult the alert log alert_SID.log and the trace file ora_SID_number.trc to check that no errors have occurred.

Using Materialized Views with Partitioned Tables

A major maintenance component of a data warehouse is synchronizing (refreshing) the materialized views when the detail data changes. Partitioning the underlying detail tables can reduce the amount of time taken to perform the refresh task. This is possible because partitioning enables refresh to use parallel DML to update the materialized view. Also, it enables the use of partition change tracking.

Materialized View Fast Refresh with Partition Change Tracking

In a data warehouse, changes to the detail tables can often entail partition maintenance operations, such as DROP, EXCHANGE, MERGE, and ADDPARTITION. To maintain the materialized view after such operations used to require manual maintenance (see also CONSIDERFRESH) or complete refresh. You now have the option of using an addition to fast refresh known as partition change tracking (PCT) refresh.

For PCT to be available, the detail tables must be partitioned. The partitioning of the materialized view itself has no bearing on this feature. If PCT refresh is possible, it occurs automatically and no user intervention is required in order for it to occur. See "About Partition Change Tracking" for PCT requirements.

As can be seen from the partial sample output from EXPLAIN_MVIEW, any partition maintenance operation performed on the sales table allows PCT fast refresh. However, PCT is not possible after partition maintenance operations or updates to the products table as there is insufficient information contained in cust_mth_sales_mv for PCT refresh to be possible. Note that the times table is not partitioned and hence can never allow for PCT refresh. Oracle Database applies PCT refresh if it can determine that the materialized view has sufficient information to support PCT for all the updated tables. You can verify which partitions are fresh and stale with views such as DBA_MVIEWS and DBA_MVIEW_DETAIL_PARTITION.

Fast refresh automatically performs a PCT refresh as it is the only fast refresh possible in this scenario. However, fast refresh will not occur if a partition maintenance operation occurs when any update has taken place to a table on which PCT is not enabled. This is shown in "PCT Fast Refresh for Materialized Views: Scenario 2".

PCT Fast Refresh for Materialized Views: Scenario 2

In this scenario, the first three steps are the same as in "PCT Fast Refresh for Materialized Views: Scenario 1". Then, the SPLIT partition operation to the sales table is performed, but before the materialized view refresh occurs, records are inserted into the times table.

The materialized view is not fast refreshable because DML has occurred to a table on which PCT fast refresh is not possible. To avoid this occurring, Oracle recommends performing a fast refresh immediately after any partition maintenance operation on detail tables for which partition tracking fast refresh is available.

If the situation in "PCT Fast Refresh for Materialized Views: Scenario 2" occurs, there are two possibilities; perform a complete refresh or switch to the CONSIDERFRESH option outlined in the following, if suitable. However, it should be noted that CONSIDERFRESH and partition change tracking fast refresh are not compatible. Once the ALTERMATERIALIZEDVIEWcust_mth_sales_mvCONSIDERFRESH statement has been issued, PCT refresh is no longer be applied to this materialized view, until a complete refresh is done. Moreover, you should not use CONSIDERFRESH unless you have taken manual action to ensure that the materialized view is indeed fresh.

A common situation in a data warehouse is the use of rolling windows of data. In this case, the detail table and the materialized view may contain say the last 12 months of data. Every month, new data for a month is added to the table and the oldest month is deleted (or maybe archived). PCT refresh provides a very efficient mechanism to maintain the materialized view in this case.

PCT Fast Refresh for Materialized Views: Scenario 3

The new data is usually added to the detail table by adding a new partition and exchanging it with a table containing the new data.

Fast refresh will automatically detect that PCT is available and perform a PCT refresh.

Using Partitioning to Improve Data Warehouse Refresh

ETL (Extraction, Transformation and Loading) is done on a scheduled basis to reflect changes made to the original source system. During this step, you physically insert the new, clean data into the production data warehouse schema, and take all of the other steps necessary (such as building indexes, validating constraints, taking backups) to make this new data available to the end users. Once all of this data has been loaded into the data warehouse, the materialized views have to be updated to reflect the latest data.

The partitioning scheme of the data warehouse is often crucial in determining the efficiency of refresh operations in the data warehouse load process. In fact, the load process is often the primary consideration in choosing the partitioning scheme of data warehouse tables and indexes.

The partitioning scheme of the largest data warehouse tables (for example, the fact table in a star schema) should be based upon the loading paradigm of the data warehouse.

Most data warehouses are loaded with new data on a regular schedule. For example, every night, week, or month, new data is brought into the data warehouse. The data being loaded at the end of the week or month typically corresponds to the transactions for the week or month. In this very common scenario, the data warehouse is being loaded by time. This suggests that the data warehouse tables should be partitioned on a date column. In our data warehouse example, suppose the new data is loaded into the sales table every month. Furthermore, the sales table has been partitioned by month. These steps show how the load process proceeds to add the data for a new month (January 2001) to the table sales.

Place the new data into a separate table, sales_01_2001. This data can be directly loaded into sales_01_2001 from outside the data warehouse, or this data can be the result of previous data transformation operations that have already occurred in the data warehouse. sales_01_2001 has the exact same columns, data types, and so forth, as the sales table. Gather statistics on the sales_01_2001 table.

Create indexes and add constraints on sales_01_2001. Again, the indexes and constraints on sales_01_2001 should be identical to the indexes and constraints on sales. Indexes can be built in parallel and should use the NOLOGGING and the COMPUTESTATISTICS options. For example:

If the partitioned table sales has a primary or unique key that is enforced with a global index structure, ensure that the constraint on sales_pk_jan01 is validated without the creation of an index structure, as in the following:

The creation of the constraint with ENABLE clause would cause the creation of a unique index, which does not match a local index structure of the partitioned table. You must not have any index structure built on the nonpartitioned table to be exchanged for existing global indexes of the partitioned table. The exchange command would fail.

Add the sales_01_2001 table to the sales table.

In order to add this new data to the sales table, you must do two things. First, you must add a new partition to the sales table. You use an ALTERTABLE ... ADDPARTITION statement. This adds an empty partition to the sales table:

The EXCHANGE operation preserves the indexes and constraints that were already present on the sales_01_2001 table. For unique constraints (such as the unique constraint on sales_transaction_id), you can use the UPDATEGLOBALINDEXES clause, as shown previously. This automatically maintains your global index structures as part of the partition maintenance operation and keep them accessible throughout the whole process. If there were only foreign-key constraints, the exchange operation would be instantaneous.

Note that, if you use synchronous refresh, instead of performing Step 3, you must register the sales_01_2001 table using the DBMS_SYNC_REFRESH.REGISTER_PARTITION_OPERATION package. See Synchronous Refresh for more information.

The benefits of this partitioning technique are significant. First, the new data is loaded with minimal resource utilization. The new data is loaded into an entirely separate table, and the index processing and constraint processing are applied only to the new partition. If the sales table was 50 GB and had 12 partitions, then a new month's worth of data contains approximately four GB. Only the new month's worth of data must be indexed. None of the indexes on the remaining 46 GB of data must be modified at all. This partitioning scheme additionally ensures that the load processing time is directly proportional to the amount of new data being loaded, not to the total size of the sales table.

Second, the new data is loaded with minimal impact on concurrent queries. All of the operations associated with data loading are occurring on a separate sales_01_2001 table. Therefore, none of the existing data or indexes of the sales table is affected during this data refresh process. The sales table and its indexes remain entirely untouched throughout this refresh process.

Third, in case of the existence of any global indexes, those are incrementally maintained as part of the exchange command. This maintenance does not affect the availability of the existing global index structures.

The exchange operation can be viewed as a publishing mechanism. Until the data warehouse administrator exchanges the sales_01_2001 table into the sales table, end users cannot see the new data. Once the exchange has occurred, then any end user query accessing the sales table is immediately able to see the sales_01_2001 data.

Partitioning is useful not only for adding new data but also for removing and archiving data. Many data warehouses maintain a rolling window of data. For example, the data warehouse stores the most recent 36 months of sales data. Just as a new partition can be added to the sales table (as described earlier), an old partition can be quickly (and independently) removed from the sales table. These two benefits (reduced resources utilization and minimal end-user impact) are just as pertinent to removing a partition as they are to adding a partition.

Removing data from a partitioned table does not necessarily mean that the old data is physically deleted from the database. There are two alternatives for removing old data from a partitioned table. First, you can physically delete all data from the database by dropping the partition containing the old data, thus freeing the allocated space:

ALTER TABLE sales DROP PARTITION sales_01_1998;

Also, you can exchange the old partition with an empty table of the same structure; this empty table is created equivalent to steps 1 and 2 described in the load process. Assuming the new empty table stub is named sales_archive_01_1998, the following SQL statement empties partition sales_01_1998:

Note that the old data is still existent as the exchanged, nonpartitioned table sales_archive_01_1998.

If the partitioned table was setup in a way that every partition is stored in a separate tablespace, you can archive (or transport) this table using Oracle Database's transportable tablespace framework before dropping the actual data (the tablespace). See "Transportation Using Transportable Tablespaces" for further details regarding transportable tablespaces.

In some situations, you might not want to drop the old data immediately, but keep it as part of the partitioned table; although the data is no longer of main interest, there are still potential queries accessing this old, read-only data. You can use Oracle's data compression to minimize the space usage of the old data. You also assume that at least one compressed partition is already part of the partitioned table.

Data Warehouse Refresh Scenarios

A typical scenario might not only need to compress old data, but also to merge several old partitions to reflect the granularity for a later backup of several merged partitions. Let us assume that a backup (partition) granularity is on a quarterly base for any quarter, where the oldest month is more than 36 months behind the most recent month. In this case, you are therefore compressing and merging sales_01_1998, sales_02_1998, and sales_03_1998 into a new, compressed partition sales_q1_1998.

Create the new merged partition in parallel in another tablespace. The partition is compressed as part of the MERGE operation:

Create the equivalent index structure for table sales_q1_1998_out than for the existing table sales.

Prepare the existing table sales for the exchange with the new compressed table sales_q1_1998_out. Because the table to be exchanged contains data actually covered in three partitions, you have to create one matching partition, having the range boundaries you are looking for. You simply have to drop two of the existing partitions. Note that you have to drop the lower two partitions sales_01_1998 and sales_02_1998; the lower boundary of a range partition is always defined by the upper (exclusive) boundary of the previous partition:

Both methods apply to slightly different business scenarios: Using the MERGEPARTITION approach invalidates the local index structures for the affected partition, but it keeps all data accessible all the time. Any attempt to access the affected partition through one of the unusable index structures raises an error. The limited availability time is approximately the time for re-creating the local bitmap index structures. In most cases, this can be neglected, because this part of the partitioned table should not be accessed too often.

The CTAS approach, however, minimizes unavailability of any index structures close to zero, but there is a specific time window, where the partitioned table does not have all the data, because you dropped two partitions. The limited availability time is approximately the time for exchanging the table. Depending on the existence and number of global indexes, this time window varies. Without any existing global indexes, this time window is a matter of a fraction to few seconds.

These examples are a simplification of the data warehouse rolling window load scenario. Real-world data warehouse refresh characteristics are always more complex. However, the advantages of this rolling window approach are not diminished in more complex scenarios.

Note that before you add single or multiple compressed partitions to a partitioned table for the first time, all local bitmap indexes must be either dropped or marked unusable. After the first compressed partition is added, no additional actions are necessary for all subsequent operations involving compressed partitions. It is irrelevant how the compressed partitions are added to the partitioned table.

Partitioning for Refreshing Data Warehouses: Scenario 1

Data is loaded daily. However, the data warehouse contains two years of data, so that partitioning by day might not be desired.

The solution is to partition by week or month (as appropriate). Use INSERT to add the new data to an existing partition. The INSERT operation only affects a single partition, so the benefits described previously remain intact. The INSERT operation could occur while the partition remains a part of the table. Inserts into a single partition can be parallelized:

The indexes of this sales partition is maintained in parallel as well. An alternative is to use the EXCHANGE operation. You can do this by exchanging the sales_01_2001 partition of the sales table and then using an INSERT operation. You might prefer this technique when dropping and rebuilding indexes is more efficient than maintaining them.

Partitioning for Refreshing Data Warehouses: Scenario 2

New data feeds, although consisting primarily of data for the most recent day, week, and month, also contain some data from previous time periods.

Solution 1

Use parallel SQL operations (such as CREATETABLE ... ASSELECT) to separate the new data from the data in previous time periods. Process the old data separately using other techniques.

New data feeds are not solely time based. You can also feed new data into a data warehouse with data from multiple operational systems on a business need basis. For example, the sales data from direct channels may come into the data warehouse separately from the data from indirect channels. For business reasons, it may furthermore make sense to keep the direct and indirect data in separate partitions.

Solution 2

Oracle supports composite range-list partitioning. The primary partitioning strategy of the sales table could be range partitioning based on time_id as shown in the example. However, the subpartitioning is a list based on the channel attribute. Each subpartition can now be loaded independently of each other (for each distinct channel) and added in a rolling window operation as discussed before. The partitioning strategy addresses the business needs in the most optimal manner.

Optimizing DML Operations During Refresh

Implementing an Efficient MERGE Operation

Commonly, the data that is extracted from a source system is not simply a list of new records that needs to be inserted into the data warehouse. Instead, this new data set is a combination of new records as well as modified records. For example, suppose that most of data extracted from the OLTP systems will be new sales transactions. These records are inserted into the warehouse's sales table, but some records may reflect modifications of previous transactions, such as returned merchandise or transactions that were incomplete or incorrect when initially loaded into the data warehouse. These records require updates to the sales table.

As a typical scenario, suppose that there is a table called new_sales that contains both inserts and updates that are applied to the sales table. When designing the entire data warehouse load process, it was determined that the new_sales table would contain records with the following semantics:

If a given sales_transaction_id of a record in new_sales already exists in sales, then update the sales table by adding the sales_dollar_amount and sales_quantity_sold values from the new_sales table to the existing row in the sales table.

Otherwise, insert the entire new record from the new_sales table into the sales table.

This UPDATE-ELSE-INSERT operation is often called a merge. A merge can be executed using one SQL statement.

In addition to using the MERGE statement for unconditional UPDATEELSEINSERT functionality into a target table, you can also use it to:

Perform an UPDATE only or INSERT only statement.

Apply additional WHERE conditions for the UPDATE or INSERT portion of the MERGE statement.

The UPDATE operation can even delete rows if a specific condition yields true.

Example 7-6 Omitting the INSERT Clause

In some data warehouse applications, it is not allowed to add new rows to historical information, but only to update them. It may also happen that you do not want to update but only insert new information. The following example demonstrates INSERT-only with UPDATE-only functionality:

When the INSERT clause is omitted, Oracle Database performs a regular join of the source and the target tables. When the UPDATE clause is omitted, Oracle Database performs an antijoin of the source and the target tables. This makes the join between the source and target table more efficient.

Example 7-8 Skipping the UPDATE Clause

In some situations, you may want to skip the UPDATE operation when merging a given row into the table. In this case, you can use an optional WHERE clause in the UPDATE clause of the MERGE. As a result, the UPDATE operation only executes when a given condition is true. The following statement illustrates an example of skipping the UPDATE operation:

This shows how the UPDATE operation would be skipped if the condition P.PROD_STATUS <> "OBSOLETE" is not true. The condition predicate can refer to both the target and the source table.

Example 7-9 Conditional Inserts with MERGE Statements

You may want to skip the INSERT operation when merging a given row into the table. So an optional WHERE clause is added to the INSERT clause of the MERGE. As a result, the INSERT operation only executes when a given condition is true. The following statement offers an example:

This example shows that the INSERT operation would be skipped if the condition S.PROD_STATUS <> "OBSOLETE" is not true, and INSERT only occurs if the condition is true. The condition predicate can refer to the source table only. The condition predicate can only refer to the source table.

Example 7-10 Using the DELETE Clause with MERGE Statements

You may want to cleanse tables while populating or updating them. To do this, you may want to consider using the DELETE clause in a MERGE statement, as in the following example:

Thus when a row is updated in products, Oracle checks the delete condition D.PROD_STATUS = "OBSOLETE", and deletes the row if the condition yields true.

The DELETE operation is not as same as that of a complete DELETE statement. Only the rows from the destination of the MERGE can be deleted. The only rows that are affected by the DELETE are the ones that are updated by this MERGE statement. Thus, although a given row of the destination table meets the delete condition, if it does not join under the ON clause condition, it is not deleted.

Example 7-11 Unconditional Inserts with MERGE Statements

You may want to insert all of the source rows into a table. In this case, the join between the source and target table can be avoided. By identifying special constant join conditions that always result to FALSE, for example, 1=0, such MERGE statements are optimized and the join condition are suppressed.

Maintaining Referential Integrity in Data Warehouses

In some data warehousing environments, you might want to insert new data into tables in order to guarantee referential integrity. For example, a data warehouse may derive sales from an operational system that retrieves data directly from cash registers. sales is refreshed nightly. However, the data for the product dimension table may be derived from a separate operational system. The product dimension table may only be refreshed once for each week, because the product table changes relatively slowly. If a new product was introduced on Monday, then it is possible for that product's product_id to appear in the sales data of the data warehouse before that product_id has been inserted into the data warehouses product table.

Although the sales transactions of the new product may be valid, this sales data do not satisfy the referential integrity constraint between the product dimension table and the sales fact table. Rather than disallow the new sales transactions, you might choose to insert the sales transactions into the sales table. However, you might also wish to maintain the referential integrity relationship between the sales and product tables. This can be accomplished by inserting new rows into the product table as placeholders for the unknown products.

As in previous examples, assume that the new data for the sales table is staged in a separate table, new_sales. Using a single INSERT statement (which can be parallelized), the product table can be altered to reflect the new products:

Purging Data from Data Warehouses

Occasionally, it is necessary to remove large amounts of data from a data warehouse. A very common scenario is the rolling window discussed previously, in which older data is rolled out of the data warehouse to make room for new data.

However, sometimes other data might need to be removed from a data warehouse. Suppose that a retail company has previously sold products from XYZSoftware, and that XYZSoftware has subsequently gone out of business. The business users of the warehouse may decide that they are no longer interested in seeing any data related to XYZSoftware, so this data should be deleted.

One approach to removing a large volume of data is to use parallel delete as shown in the following statement:

DELETE FROM sales WHERE sales_product_id IN (SELECT product_id
FROM product WHERE product_category = 'XYZ Software');

This SQL statement spawns one parallel process for each partition. This approach is much more efficient than a series of DELETE statements, and none of the data in the sales table needs to be moved. However, this approach also has some disadvantages. When removing a large percentage of rows, the DELETE statement leaves many empty row-slots in the existing partitions. If new data is being loaded using a rolling window technique (or is being loaded using direct-path INSERT or load), then this storage space is not reclaimed. Moreover, even though the DELETE statement is parallelized, there might be more efficient methods. An alternative method is to re-create the entire sales table, keeping the data for all product categories except XYZSoftware.